the Nest of Students

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn: Fundamental concepts and applications of machine learning + Advantages and shortcomings of widely used machine learning algorithms + How to represent data processed by machine learning, including which data aspects to focus on + Advanced methods for model evaluation and parameter tuning + The concept of pipelines for chaining models and encapsulating your workflow + Methods for working with text data, including text-specific processing techniques + Suggestions for improving your machine learning and data science skills.

In the years since the development and subsequent success of Stone Matrix Asphalt (SMA), a plethora of articles have emerged, scattered throughout various publications. The time is right for a comprehensive resource that collects, examines, and organizes this information and makes it easily accessible. A compilation and distillation of the latest knowledge available, Stone Matrix Asphalt: Theory and Practice describes SMA, from its origin through implementation, and explores various methods and design approaches. Detailing the requirements and materials for the mix, including asphalt binder, aggregates, and stabilizers, the book covers standard and alternative design methods and their execution, as well as best practices and production processes. It addresses issues such as permanent deformation and fatigue resistance, as well as special applications, such as bridge deck surfacing and airfields. The book uses practical examples to illustrate theory, presents practical tips useful in everyday work, and includes a review of problems that can occur during laydown, and their potential solutions.